Inference and learning in probabilistic logic programs using weighted Boolean formulas

@article{Fierens2015InferenceAL,
  title={Inference and learning in probabilistic logic programs using weighted Boolean formulas},
  author={Daan Fierens and Guy Van den Broeck and Joris Renkens and Dimitar Sht. Shterionov and Bernd Gutmann and Ingo Thon and Gerda Janssens and Luc De Raedt},
  journal={TPLP},
  year={2015},
  volume={15},
  pages={358-401}
}
Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks, such as computing the marginals, given evidence and learning from (partial) interpretations, have not really been addressed for probabilistic logic programs before. The first contribution of this paper is a suite… CONTINUE READING
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